Your data and AI platform shouldn't hold you back. We architect, build, and optimise Snowflake data platforms — with Marketplace, Data Sharing, and Delta Lake — so your teams ship models, not tickets.
The Reality
Your teams aren't failing because they lack talent. They're failing because the platform underneath them was never designed for AI-scale workloads. These problems compound — and they're costing you millions in wasted compute and lost opportunity.
Your data lives in 5 different systems — Snowflake, S3, on-prem databases, legacy warehouses — and nobody trusts a single number.
Data scientists build models in notebooks. Engineering can't deploy them. Most ML projects never make it past the prototype stage.
No unified catalog. No lineage. No access controls that work across data and AI assets. Compliance audits are a scramble every quarter.
Clusters running 24/7, no autoscaling, no job-level cost attribution. Your cloud bill grows meaningfully year-over-year and nobody knows why.
ETL jobs fail overnight. Nobody notices until a dashboard shows stale data on Monday morning. There's no alerting, no SLA monitoring.
Batch pipelines run once a day. Your fraud detection, pricing engine, and recommendation system are always 24 hours behind.
Tableau and Power BI query raw tables instead of a governed semantic layer. Every analyst writes their own SQL — and gets different answers.
Models are deployed with no versioning, no lineage, and no audit trail. You can't explain to a regulator what your model does or why.
Our Expertise
Design and deploy a unified data platform that replaces fragmented data warehouses and data lakes with a single, governed platform built on Delta Lake.
Build production-grade ML pipelines with Data Sharing — from experiment tracking and model registry to automated retraining and monitoring.
Build reliable, scalable data pipelines with Spark, Delta Live Tables, and Snowflake Workflows — from ingestion to transformation.
Deploy and manage large language models on Snowflake — fine-tuning, RAG pipelines, vector search, and production serving with governance.
Snowflake + AI
Snowflake isn't just a data warehouse — it's an AI platform. From feature engineering and model training to LLM fine-tuning and real-time inference, we build the infrastructure that turns your data into production AI.
Explore AI CapabilitiesGo beyond SQL and dashboards. Bring your own models into Snowflake — or use built-in AI functions for classification, summarisation, and entity extraction.
Track every experiment, compare model runs, and promote the best performers to production — all within a governed, reproducible workflow.
Govern data, models, and AI assets from a single control plane. Fine-grained access, lineage tracking, and compliance — built in, not bolted on.
Deploy models to production endpoints with autoscaling, A/B testing, and real-time monitoring — so your AI delivers value, not just predictions.
Snowflake Ecosystem
Snowflake doesn't operate in isolation. We integrate it with your entire data ecosystem — ingestion, transformation, governance, BI, and AI serving — so every layer works together.
+ native connectors to data sources via Snowflake Partner Connect
Our Process
We audit your current data infrastructure — sources, pipelines, governance gaps, and ML readiness — and deliver a prioritised data platform roadmap.
We design your data platform architecture: medallion layers, Delta Lake schemas, Marketplace policies, and compute strategy — tailored to your workloads.
Our engineers build the pipelines, migrate data from legacy systems, implement Delta Live Tables, and deploy Data Sharing for model tracking.
We go live — connecting Snowflake to your BI layer (Tableau, Power BI), downstream applications, and alerting infrastructure.
Post-launch, we tune cluster configurations, optimise query performance, reduce cloud costs, and expand the platform as your AI ambitions grow.
Case Study
A global pharmaceutical company was running 12 disconnected data sources across 3 cloud providers. ML model deployment took 6 months. We built a unified data platform on Snowflake, implemented Marketplace for GDPR compliance, and deployed Data Sharing — cutting model deployment time by 75%.
4×
Faster ML deployment
90%
Less data duplication
12→1
Unified platform
GDPR
Compliant from day one
Faster ML model deployment
Unified · Governed · AI-Ready
How We Work
Pick the model that fits where you are. All engagements include a dedicated Snowflake lead and a clear outcome definition.
Ideal for: Teams already on Snowflake who need an expert audit
A 2-week deep dive into your Snowflake environment — cluster config, pipeline health, Marketplace setup, and cost efficiency — with a prioritised improvement plan.
Ideal for: Organisations migrating to or building on Snowflake
A full data platform build — from architecture and migration through to Data Sharing, governance, and BI integration — delivered in 8–12 weeks with a dedicated engineering team.
Ideal for: Teams that want expert-managed Snowflake operations
We manage your Snowflake platform end-to-end — monitoring, optimisation, ML pipeline operations, and a dedicated engineering partner on call.
Common Questions
Snowflake is the cloud-native data platform that decoupled storage from compute, enabling elastic scaling, multi-cluster concurrency, and zero-copy data sharing across organisations. It now spans data warehousing, lakehouse capabilities, Snowpark for Python / Java / Scala workloads, Cortex AI (managed LLMs and ML), Streamlit-based data apps, and tight integration with Salesforce via Data Cloud federation. Decision Foundry is a Snowflake Select Partner.
Data platform architecture and modernisation; Snowflake-specific design patterns (medallion layers, dynamic tables, streams + tasks, zero-copy cloning for environments); data pipeline operations (Snowpipe, Streams, native ingestion); MLOps and AI platform engineering (Cortex, ML Functions); LLMOps and generative AI engineering (Cortex AISQL, Document AI); data sharing and Snowflake Marketplace publishing; and ongoing cost / performance optimisation.
Snowflake leads on SQL ease of use, multi-cluster concurrency, governance via row-access policies, and zero-copy integration with Salesforce Data Cloud. Databricks leads on ML, streaming, and engineering. BigQuery leads on Google ecosystem integration and serverless pricing simplicity. Snowflake's sweet spot is the BI + warehouse layer; many enterprises pair it with Databricks for engineering / ML.
A focused Snowflake stand-up (account setup, 5–10 data sources ingested, medallion layering, basic governance, BI integration) runs 10–14 weeks. A full enterprise build with multi-region, complex governance, Cortex AI activation, and data sharing partnerships runs 6–10 months. Snowflake cost scales with compute consumption (credits) and storage — we design for cost-efficiency from day one. Discovery call first.
Yes, this is one of our most common engagements. Snowflake cost-runaway usually traces to a handful of issues: oversized warehouses, missing auto-suspend, query patterns that scan more than they need, materialised views or caching not used, and the lack of resource monitors. We run a 4-6 week cost audit and typically identify 30-50% in addressable savings without sacrificing performance.
Snowflake Select Partner with deep Cortex AI, Snowpark, and data-sharing expertise. Plus the broader enterprise data stack — Databricks Premier Partner, Salesforce SELECT, AWS / Azure / GCP certifications. Most Snowflake consultancies are Snowflake-only; we're cross-stack, which matters when Snowflake needs to connect to Salesforce, MCI, Databricks, or AI agents downstream. 200+ data projects delivered.